Majority vote ensemble classifier for accurate detection of credit card frauds

Mammography is currently the most effective imaging modality for early detection of breast cancer. In a CAD system for masses based on mammography, a mammogram is segmented to detect the masses. The segmentation gives rise to mass regions of interested (ROIs), which are either benign or malignant. There is a need to classify the extracted mass ROIs into benign and malignant masses; it is a hard problem because the texture micro-structures of benign and malignant masses have close resemblance. In this paper, a method for classifying mass ROIs into benign and malignant masses is presented. The key idea of the proposal is to build an ensemble classifier that employs Gabor features, consults different experts (classifiers) and takes the final decision based on majority vote. The system is evaluated on 512 (256 benign+256 malignant) mass ROIs extracted from mammograms of DDSM database. The ensemble classifier improves the classification rate for the problem of the discrimination of benign and malignant masses to 90.64%. Comparison with state-of-the-art techniques suggests that the proposed system outperforms similar methods.

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Twitter Sentiment Analysis Using an Ensemble Majority Vote Classifier

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.55.1.9 ◽

2020 ◽

Vol 55 (1) ◽

Cited By ~ 2

Author(s):

Alaa Khudhair Abbas ◽

Ali Khalil Salih ◽

Harith A. Hussein ◽

Qasim Mohammed Hussein ◽

Saba Alaa Abdulwahhab

Keyword(s):

Social Media ◽

Naive Bayes ◽

Majority Vote ◽

Ensemble Classifier ◽

Classification Performance ◽

Naïve Bayes ◽

Classification Model ◽

Media Messages ◽

Individual Classifier ◽

The Individual

Twitter social media data generally uses ambiguous text that can cause difficulty in identifying positive or negative sentiments. There are more than one billion social media messages that need to be stored in a proper database and processed correctly to analyze them. In this paper, an ensemble majority vote classifier to enhance sentiment classification performance and accuracy is proposed. The proposed classification model is combined with four classifiers, using varying techniques—naive Bayes, decision trees, multilayer perceptron and logistic regression—to form a single ensemble classifier. In addition to these, a comparison is drawn among the four classifiers to evaluate the performance of the individual classifiers. The result shows that in terms of an individual classifier, the naive Bayes classifier is optimal as compared to the others. However, for comparing the proposed ensemble majority vote classifier with the four individual classifiers, the result illustrates that the performance of the proposed classifier is better than the independent one.

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The Making of a Watchdog

Watchdog ◽

10.1093/oso/9780197502990.003.0004 ◽

2020 ◽

pp. 40-54

Author(s):

Richard Cordray

Keyword(s):

Credit Card ◽

Student Loans ◽

Majority Vote ◽

The Political ◽

Financial Protection ◽

Simple Majority ◽

Financial Industry ◽

Political Opposition ◽

President Obama ◽

Simple Majority Vote

The Consumer Financial Protection Bureau’s strategy was to push through the political opposition by acting aggressively for consumers. Early on, the bureau worked to make the terms of financial products more understandable for consumers, creating streamlined forms for mortgages, student loans, and credit cards. It took major enforcement actions against credit card companies for deceptive marketing, returning billions of dollars to consumers. As Cordray’s nomination languished in the Senate, President Obama made an extraordinary recess appointment to install him on a temporary basis. The financial industry immediately challenged the appointment in court, and Republicans pushed back hard in tough oversight hearings. In July 2013, the Democratic Senate majority leader, Harry Reid, moved to invoke the “nuclear option” to approve nominations by a simple majority vote. The Republicans yielded, and Cordray was confirmed in a bipartisan vote of sixty-six to thirty-four. In a tough two-year battle, the bureau prevailed over the strenuous opposition.

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